Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,087
datasets available to search
ShareScore release 0.9.0
Dataset results
1,087 results for “Chest”
Golden treasure chest
One of my very first own 3D models, feel free to use it. Source: Objaverse 1.0 / Sketchfab
A chest
A wooden chest I made for class. Source: Objaverse 1.0 / Sketchfab
CHEST V2
Includes clean textures … (ノಠ益ಠ)ノ彡┻━┻ Source: Objaverse 1.0 / Sketchfab
The chest.
**Old chest** Source: Objaverse 1.0 / Sketchfab
Chest
Old Pirate Chest Source: Objaverse 1.0 / Sketchfab
Jenner's Medicine Chest
This medicine chest, made in the 1780s, was owned by a doctor called Edward Jenner, who is famous as a pioneer in vaccination. He invented the world's first vaccine, against smallpox – a disease that was responsible for one in ten deaths in Britain at the time. As a doctor, Jenner would have carried this chest with him when visiting patients in their homes. Use our 3D Jenner's medicine chest model in the classroom to bring your curriculum teaching to life: https://learning-resources.sciencemuseum.org.uk/resources/jenners-medicine-chest Find out more in the Science Museum Group online collection: https://collection.sciencemuseumgroup.org.uk/objects/co8619246/ Source: Objaverse 1.0 / Sketchfab
chest
lowpoly chest made in blender Source: Objaverse 1.0 / Sketchfab
[XYZ School] HW5 CHEST
Source: Objaverse 1.0 / Sketchfab
VinDr Chest X-ray Datset
<p>This zip file contains train and test data of VinDr Chest X-ray images.</p>
Chest X-Ray Image Dataset: A Resource for Medical Diagnosis and Machine Learning
<p>The Chest X-Ray Image Dataset is an extensive collection designed to support medical research and the development of diagnostic tools for COVID-19 detection. It consists of two distinct classes: COVID-19 affected X-ray images and normal X-ray images of the chest area, each covering the full lungs. This dataset provides a diverse range of X-ray images, capturing the unique characteristics of both healthy and COVID-19 affected lungs, making it an invaluable resource for training and testing machine learning models in medical image classification and analysis.</p>
FIGURE 2 in Paraclaravis, a new genus for the Purple-winged and Maroon-chested Ground-doves (Aves: Columbidae)
FIGURE 2. External morphology of adult male and female Claravis pretiosa, Paraclaravis mondetoura and P. geoffroyi (Martin Elliott, reproduced with permission from Handbook of the Birds of the World, del Hoyo et al. 2017). The plate illustrates four diagnostic character states differentiating Paraclaravis from Claravis: lesser upperwing-coverts marked with a single bar, unmarked tertials, white distal parts of the outer tail feathers, and pale under tail-coverts.
FIGURE 1. 50 in Paraclaravis, a new genus for the Purple-winged and Maroon-chested Ground-doves (Aves: Columbidae)
FIGURE 1. 50% majority-rule consensus tree of Claravinae from (a) mitochondrial (COI, ND2, ATP8, CytB) and (b) nuclear (FIB7) DNA sequences. Bootstrap support values are indicated above the branches. Species traditionally classified in the genus Claravis are highlighted. Taxonomic names followed by an asterisk (*) indicate species that are illustrated. All bird paintings are by Martin Elliott and Lyn Wells, reproduced with permission from Handbook of the Birds of the World (del Hoyo et al. 2017).
Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software.
<p>We uploaded the complete database of the manuscript: Grassi R, Cappabianca S, Urraro F, Feragalli B, Montanelli A, Patelli G, Granata V, Giacobbe G, Russo GM, Grillo A, De Lisio A, Paura C, Clemente A, Gagliardi G, Magliocchetti S, Cozzi D, Fusco R, Belfiore MP, Grassi R, Miele V. Chest CT Computerized Aided Quantification of PNEUMONIA Lesions in COVID-19 Infection: A Comparison among Three Commercial Software. Int J Environ Res Public Health. 2020 Sep 22;17(18):6914. doi: 10.3390/ijerph17186914. PMID: 32971756; PMCID: PMC7558768.</p>
Coronavirus Disease 2019 (COVID-19) in Italy: features on Chest Computed Tomography using a structured report system
<p>We uploaded a dataset including the presence of GGO and its distribution for each patient presented in the manuscript: Grassi R, Fusco R, Belfiore MP, Montanelli A, Patelli G, Urraro F, Petrillo A, Granata V, Sacco P, Mazzei MA, Feragalli B, Reginelli A, Cappabianca S. Coronavirus disease 2019 (COVID-19) in Italy: features on chest computed tomography using a structured report system. Sci Rep. 2020 Oct 14;10(1):17236. doi: 10.1038/s41598-020-73788-5. Erratum in: Sci Rep. 2021 Feb 15;11(1):4231. PMID: 33057039; PMCID: PMC7566610.</p>
Chest Radiograph at Diverse Institutes (CRADI) dataset
<p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p> </p> <p><strong>Data source</strong></p> <p><strong>Number of images</strong></p> <p><strong>Data_source</strong></p> <p>Academic hospital 1</p> <p>74,082</p> <p>0</p> <p>In- and out-patient from Academic hospital 2</p> <p>5,996</p> <p>1</p> <p>Screening participants from Academic hospital 2</p> <p>2,130</p> <p>2</p> <p>Community clinics</p> <p>1,804</p> <p>3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p> </p> <p> </p> <p> </p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Data organization</strong></p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data_resource or data_source?</p> <p>同上</p> <p><strong>Introduction to the Chest Radiograph at Diverse Institutes (CRADI) dataset</strong></p> <p> </p> <p><strong>Background</strong></p> <p> </p> <p>Chest radiography is extensively used to screen and diagnose pulmonary and cardiac diseases. The advantages of its clinical practicality, efficiency, and cost-effectiveness make chest radiography the most accessible imaging test for pulmonary disorders, especially in primary hospitals. Currently, the interpretation of a chest radiograph mainly relies on radiologists.</p> <p> </p> <p>With the development of algorithms, convolutional neural networks (CNNs) have shown the ability to detect a single disorder in chest radiography, e.g., pneumothorax, lung cancer, pneumonia, and tuberculosis. Traditionally, expert annotation is applied to establish a CNN model for classifying medical images. This manual labeling procedure is time-consuming and highly demanding. Importantly, beyond the detection of a single disease, multi-label classification is necessary to interpret a chest radiograph in clinical practice.</p> <p> </p> <p>In order to promote the development of the artificial intelligence-assisted diagnosis of chest radiography, we launched the Chest Radiograph at Diverse Institutes (CRADI) dataset. This dataset is comprised of a large number of chest radiographs. Each radiograph has a 25-label disorder annotation, that was established by the terms adopted from the Fleischner’s glossary, and extracted from the original diagnostic report by natural language processing (NLP) and radiologist expertise.</p> <p> </p> <p>At present, the data of the CRADI dataset comes from two academic hospitals and multiple community clinics in Shanghai. The cases in the CRADI dataset are comprised of in-patients, out-patients, and screening participants.</p> <p> </p> <p>The CRADI dataset provides a better understanding of the multiple and different clinical data sources for chest radiography, which is potentially helpful for the training and test of CNN models.</p> <p> </p> <p>We welcome more data into the CRADI dataset. If you find it helpful to your research work or you want to contribute to this dataset, please feel free to contact us.</p> <p><strong>Data source </strong></p> <p>training data data source: 0</p> <p>In- and out-patient from external hospital data source: 1</p> <p>Screening participants from external hospital data source : 2</p> <p>Community clinics datasource: 3</p> <p>Note. Each case includes one posterior-anterior (PA) view chest radiograph and the corresponding text label of disorder findings.</p> <p><strong>Preprocessing methods</strong></p> <p>Images: transformed and resized from DCM format to PNG, changed from 12-bit grayscale to 8-bit. All patient- or institute-related information is de-identified.</p> <p>Label: labels are extracted from the original diagnostic reports. The regular expression is applied by NLP and rules-based extraction methods. In total, 25 labels are extracted for each image.</p> <p><strong>Data</strong></p> <p><strong>Overview: </strong>All images are compressed into one file. All classification labels are listed in one CSV file. Data order is as following way:</p> <p><strong>Classification result</strong></p> <p>Each image links to the label by an item of ‘patientID’.</p> <p>data_resource<a href="#_msocom_1">[1]</a> stands for the resource of data. Data sources are listed in the previous table.</p> <p>Result table format: |pateintID|data_resource<a href="#_msocom_2">[2]</a> |label1|label2|.......|label25|</p> <p>The order of the 25 labels is as the following:</p> <p>1) pneumothorax, 2) emphysema, 3) pulmonary parenchymal calcification, 4) PICC implant, 5) aortic unfolding, 6) aortic arteriosclerosis, 7) aortic abnormalities, 8) small consolidation, 9) cardiomegaly, 10) patchy consolidation, 11) consolidation, 12) cavity, 13) mass, 14) prominent bronchovascular marking, 15) pulmonary edema, 16) pulmonary nodule, 17) hilar adenopathy, 18) pleural effusion, 19) pleural thickening, 20) pleural adhesion, 21) pleural calcification, 22) pleural abnormalities, 23) scoliosis, 24) pacemaker implant, 25) interstitial involvement.</p> <p>Data will be released after anonymilization process.</p>
Impact of elbow supports use on chest compression quality: a crossover simulation study
<p>Abstract<br> During chest compressions, a downward force is generated by the rescuer’s body through straightened arms to the patient’s chest. If the elbows are not straight, the depth of chest compressions could be reduced. Such a situation could occur during<br> prolonged chest compressions or when the rescuer must wear protective equipment, such as during the coronavirus disease 2019 (COVID-19) pandemic, and can lead to reduced chest compression quality. This study aimed to compare the quality of chest compressions performed using elbow supports to limit the elbows’ range of motion with that of standard chest compression (SCC). This prospective, randomized controlled, crossover simulation trial was conducted from October to December 2018. It included 34 participants who were certified in Basic Life Support with an overall compression score of ≥80% in the preliminary evaluation. They were randomly assigned to the immediate intervention (elbow support chest compression (ESCC)) or wait-list control (SCC) groups, and were asked to perform hands-only cardiopulmonary resuscitation for 8 minutes with and without elbow supports, respectively. After 1 week, the participants were made to switch groups, and the quality of chest compressions between the two groups was compared. The study findings showed that the ESCC group had a significantly higher overall compression score than the SCC group (82.85 ± 13.73% vs. 76.11 ± 19.19%, respectively, p = 0.044). No difference was observed in the chest compression depth between men and women in the ESCC group (53.12 ± 6.14 mm vs.<br> 49.13 ± 3.23 mm, respectively, p = 0.053), but a significant difference was observed between those of the SCC group (53.18 ± 6.58 mm vs. 47.87 ± 5.23 mm, respectively, p = 0.026). Thus, elbow supports could assist rescuers in performing more effective<br> chest compressions, especially for females or in situations where compression quality could be affected.</p>
Treasure Chest
***A Simple Treasure Chest.*** Source: Objaverse 1.0 / Sketchfab
Chest Coin flip - first animation in Maya
credit of model to : Derek Ford Source: Objaverse 1.0 / Sketchfab
Captain's Quarters - Norse Chest
Source: Objaverse 1.0 / Sketchfab
Coaxial Drainage Versus Standard Chest Tube After Pulmonary Lobectomy
ClinicalTrials.gov study NCT04877925. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.